Optimal dividend strategies for a compound Poisson risk process under transaction costs and power utility

We characterize the value function of maximizing the total discounted utility of dividend payments for a compound Poisson insurance risk model when strictly positive transaction costs are included, leading to an impulse control problem. We illustrate that well known simple strategies can be optimal in the case of exponential claim amounts. Finally we develop a numerical procedure to deal with general claim amount distributions.

General unknown screening procedure for the characterization of human drug metabolites: application to loratadine phase I metabolism.

This article describes the application of a recently developed general unknown screening (GUS) strategy based on LC coupled to a hybrid linear IT-triple quadrupole mass spectrometer (LC-MS/MS-LIT) for the simultaneous detection and identification of drug metabolites following in vitro incubation with human liver microsomes. The histamine H1 receptor antagonist loratadine was chosen as a model compound to demonstrate the interest of such approach, because of its previously described complex and extensive metabolism. Detection and mass spectral characterization were based on data-dependent acquisition, switching between a survey scan acquired in the ion-trapping Q3 scan mode with dynamic subtraction of background noise, and a dependent scan in the ion-trapping product ion scan mode of automatically selected parent ions. In addition, the MS(3) mode was used in a second step to confirm the structure of a few fragment ions. The sensitivity of the ion-trapping modes combined with the selectivity of the triple quadrupole modes allowed, with only one injection, the detection and identification of 17 phase I metabolites of loratadine. The GUS procedure used in this study may be applicable as a generic technique for the characterization of drug metabolites after in vitro incubation, as well as probably in vivo experiments.

The effects of gamma-hydroxybutyrate, baclofen and GABAB receptors on brain activity and sleep in mice and humans

Currently, there is an increased interest in γ-hydroxybutyric acid (GHB) and its effects onsleep. This compound, sometimes referred to as 'rape drug', was recently approved as atreatment for the sleep disorder narcolepsy. Although several studies suggest that GHBinduces slow-wave sleep duration and improves sleep quality by increasing EEG slow-waveactivity, others question its ability to induce physiological sleep. GHB's mechanism of actionis still unclear, although in vivo and in vitro it seems to act at high doses as a low-affinityagonist of GABAB receptors. Furthermore, the role GABAB receptors play in sleep and theelectroencephalogram (EEG) is largely unknown.The aim of this project was therefore to investigate the effects of GHB on sleep and EEG, theinvolvement of GABAB receptors in mediating these effects, as well as the intrinsic role ofeach GABAB receptor subunit in the regulation of sleep. Thus, we administered GHB andbaclofen (BAC, a high-affinity agonist at GABAB receptor) to mice lacking the different GABABreceptor subunits and to healthy human volunteers.Our results, both in mice and humans, showed that GHB produced slow waves exclusivelythrough the stimulation of GABAB receptors, but did not induce physiological sleepnecessary to reduce sleep need and to increase cognitive performance. Unlike GHB, BACaffected the homeostatic regulation of sleep (sleep need) and induced a delayedhypersomnia. Finally, GABAB receptor and its subunits seem to play an important role insleep and in particular its circadian distribution.

Structural macro factors and the affine term structure of interest rates

This work consists of three essays investigating the ability of structural macroeconomic models to price zero coupon U.S. government bonds. 1. A small scale 3 factor DSGE model implying constant term premium is able to provide reasonable a fit for the term structure only at the expense of the persistence parameters of the structural shocks. The test of the structural model against one that has constant but unrestricted prices of risk parameters shows that the exogenous prices of risk-model is only weakly preferred. We provide an MLE based variance-covariance matrix of the Metropolis Proposal Density that improves convergence speeds in MCMC chains. 2. Affine in observable macro-variables, prices of risk specification is excessively flexible and provides term-structure fit without significantly altering the structural parameters. The exogenous component of the SDF is separating the macro part of the model from the term structure and the good term structure fit has as a driving force an extremely volatile SDF and an implied average short rate that is inexplicable. We conclude that the no arbitrage restrictions do not suffice to temper the SDF, thus there is need for more restrictions. We introduce a penalty-function methodology that proves useful in showing that affine prices of risk specifications are able to reconcile stable macro-dynamics with good term structure fit and a plausible SDF. 3. The level factor is reproduced most importantly by the preference shock to which it is strongly and positively related but technology and monetary shocks, with negative loadings, are also contributing to its replication. The slope factor is only related to the monetary policy shocks and it is poorly explained. We find that there are gains in in- and out-of-sample forecast of consumption and inflation if term structure information is used in a time varying hybrid prices of risk setting. In-sample yield forecast are better in models with non-stationary shocks for the period 1982-1988. After this period, time varying market price of risk models provide better in-sample forecasts. For the period 2005-2008, out of sample forecast of consumption and inflation are better if term structure information is incorporated in the DSGE model but yields are better forecasted by a pure macro DSGE model.

Interest rates mapping

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well. (C) 2008 Elsevier B.V. All rights reserved.